A challenge-response test for telling apart a human from an algorithmic system is known as a Turing Test. When a computer program is able to generate such tests and evaluate the result, it is known as a Completely Automated Public Turing tests to tell Computers and Humans Apart (CAPTCHA), also known as Human Interactive Proof (HIP).
Systems for automated administration of CAPTCHAs in order to grant the challenged entity access to information content or an application interface—as in a publically accessible website—are traditionally based on presentation of images of text in which characters (typically Latin alphabet letters and digits) are arranged and distorted to prevent algorithms from segmenting the image of the text into individual characters and recognizing the latter.
The challenged entity's successful interpretation of the image and the correct sequence of alphanumeric characters being submitted as the answer is taken as a sign that the entity interacting with the system is human. This ability is useful for publically accessible websites that attempt to deny or limit access to programmable algorithmic systems (“bots”) acting as machine-implemented impostors of human users.
However, research results show that computers systems are currently as good (and sometimes better) than humans at recognizing even highly distorted single characters. Modern text-based CAPTCHAs rely on non-trivial placement of individual characters and systematic addition of noise and distortion in order to increase difficulty of separation of characters and individual character recognition. Attempts to make these systems harder to break have a natural complexity limit where the presented images are too hard for humans to decipher to be practical.
Furthermore, continuing reliance on text-based CAPTCHAs aimed at diverse user groups requires either development of additional alphabet-specific CAPTCHAs for speakers of languages with non-Latin alphabets, or further limiting the image complexity due to lower precision of character recognition of Latin characters by users who interact with Latin alphabet less frequently.
In cases where CAPTCHA images are presented to users on a screen of a handheld device, the size and level of detail of the test image—and therefore complexity and robustness of protection against an algorithmic agent—is limited further. Mobile systems that display the equivalent of user keyboard on part of a screen further reduce the eligible size of test images, while simultaneously making it more difficult to switch between characters and digits when submitting user input. In cases of multilingual users solving a CAPTCHA in alphabet other than the default setting of the device, a further usability barrier arises with need to switch between the default alphabet input mode setting and one used for input of the CAPTCHA solution string.
An additional complication in practical administration of CAPTCHAs of high complexity is necessity to present multiple tests in cases where not all test sequences are easily recognizable by users. This leads to users being frustrated by the experience of interacting with such systems and results in high level of dissatisfaction and scepticism with the overall approach.
Therefore, between continuing advances of easily available character recognition as well as segmentation algorithm implementations and inherent limitations on increasing the complexity of CAPTCHA challenge images, the gap between humans and algorithms successfully solving CAPTCHA challenges is increasingly narrowing. Conventional text-based CAPTCHA will eventually be unsuitable. Alternative solutions to text-based CAPTCHAs are needed.